AI Integration Services

AI integration is connecting AI (LLMs, RAG, agents) into the systems, products, and workflows you already run, so it actually does useful work instead of sitting in a demo. It covers the architecture, the connectors (APIs, data pipelines, enterprise platforms), the grounding in your data, and the governance to run it safely in production.

Yes. We add AI alongside what already works, with staged rollout and rollback, so nothing you depend on breaks. A senior architect designs the integration around your current stack before any code is written.

Security is built in: we use scoped, least-privilege access, secrets management, RBAC, and audit logging, and follow SOC 2, HIPAA, and GDPR practices. For sensitive data, we can run open models in your own cloud or on-prem so data never leaves your environment, the opposite of the "shadow AI" risk of staff pasting data into unsanctioned tools.

Yes. We carefully connect AI to older systems and databases, with human-in-the-loop checks so that undocumented legacy business rules are never silently "filled in" incorrectly by a model. Where a system lacks a clean API, we build a connector or middleware to bridge it.

We build model-agnostic. Providers sit behind an abstraction layer, so you can move between OpenAI, Anthropic, Google, Llama, or Mistral with a configuration change. We run our own products this way, across multiple providers in production.

Three layers: RAG grounds answers in your data; output validators check results against rules and confidence thresholds; and low-confidence or high-stakes actions escalate to a human with an approval gate. In a real workflow, a hallucination is a business incident, so we build for a measurable, tunable error rate.

Yes. We integrate AI into enterprise platforms like Salesforce, SAP, ServiceNow, HubSpot, and Snowflake, and into your own APIs, through documented, secure connections, with no brittle middleware workarounds. If a managed AI feature from the platform fits, we'll use it rather than overbuild.

Yes, completely. You own all the source code, prompts, connectors, and intellectual property, with full rights transferred to you. There's no vendor lock-in: you're free to host, extend, or move the integration whenever you want.

Per industry averages, a focused single-system integration or proof of concept typically runs from $20,000 to $80,000, while a multi-system, production-grade integration usually lands between $70,000 and $300,000+, depending on the number of systems, data readiness, and compliance scope.

Custom work doesn't fit a fixed price list, so rather than quote a misleading number, we scope your project and provide a tailored estimate after an initial consultation. Our scope generator can give you a quick ballpark in the meantime.

It depends on scope, but our architect-led, AI-accelerated delivery gets you a working proof of concept in about 1 week and a functional integration in roughly a month, then iterates from there. The first step is a Scope & Design Document that turns your goal into a build-ready plan.

For simple, common-shape automations, sometimes yes. But no-code tools struggle once you need custom business logic, supervised agents, compliance requirements, or an audit trail you can defend to a regulator. That's exactly where custom AI integration, the policy and governance layer that the platforms don't have, is worth it.

Yes. Through a dedicated team or a retained contract, we handle monitoring, model upgrades, prompt tuning, connector maintenance, and new capabilities as your systems and the models evolve. 80% of our clients stay with us for 7+ years.